Intelligent Perception Module
Includes camera sensors, radar sensors, microphone arrays, IMU inertial measurement units, and more, responsible for environmental perception, object recognition, voice interaction, and posture detection.
The year 2024 is widely regarded as the first year of humanoid robot commercialization, and the global humanoid robot industry is entering a period of unprecedented development opportunities. According to the report released at the first China Humanoid Robot Industry Conference, the Chinese humanoid robot market reached approximately RMB 2.76 billion in 2024 and is expected to grow to RMB 75 billion by 2029, accounting for 32.7% of the global total. By 2035, the market size is expected to exceed RMB 300 billion.
The year 2024 is widely regarded as the first year of humanoid robot commercialization, and the global humanoid robot industry is entering a period of unprecedented development opportunities. According to the report released at the first China Humanoid Robot Industry Conference, the Chinese humanoid robot market reached approximately RMB 2.76 billion in 2024 and is expected to grow to RMB 75 billion by 2029, accounting for 32.7% of the global total. By 2035, the market size is expected to exceed RMB 300 billion.
With the rapid breakthroughs in AI large-model technology, humanoid robots have shifted from traditional pre-programmed modes to a new stage of embodied intelligence. Large models effectively provide robots with a "brain," enabling higher-level abstract understanding and reasoning capabilities. Leading companies such as Tesla Optimus, Figure, Unitree, and UBTECH have launched new-generation products and achieved batch deployment in industrial manufacturing, warehouse logistics, and other scenarios.
As mobile terminals integrating AI technology, software algorithms, motion control, and hardware structure, humanoid robots involve core modules such as intelligent perception, power systems, decision and control, joint actuation, and dexterous hands. Each module requires rigorous testing and validation. Key testing challenges include signal integrity verification for multi-sensor fusion perception, safety testing for high-power-density power systems, reliability verification for high-speed data transmission, and motion control precision testing for precision actuators.
To address these challenges, it is necessary to establish a complete testing system covering the entire chain from intelligent perception, power endurance, and decision control to joint actuation and dexterous hand operation, ensuring that the performance indicators of each functional module meet design requirements.
By function, AI humanoid robots can be divided into six core modules:
Includes camera sensors, radar sensors, microphone arrays, IMU inertial measurement units, and more, responsible for environmental perception, object recognition, voice interaction, and posture detection.
Centered on battery packs and cell assemblies, providing power support for the whole machine, with requirements for high energy density, long endurance, and fast charging capability.
Includes intelligent chips, controllers, ECUs, and more, responsible for information processing, decision planning, and control execution.
Composed of linear actuators, rotary actuators, frameless torque motors, harmonic reducers, and more, enabling precise movement of all body parts.
Includes coreless motors, stepper motors, planetary reducers, tactile sensors, and more, enabling fine manipulation capability.
Covers auxiliary systems such as structural parts, communication devices, and heat dissipation devices.
The test plan adopts a layered architecture design. Physical-layer testing focuses on signal integrity testing of physical interfaces such as MIPI D-PHY/C-PHY, GMSL/FPD-LINK, and PCIe. Device-layer testing evaluates performance parameters of key devices such as battery cells, motors, and sensors. System-layer testing performs functional verification and reliability testing after module integration. The test flow follows a progressive principle of "device verification - module integration - system joint debugging."
The intelligent perception module test objects include camera sensors (main vision cameras, depth cameras, infrared cameras, etc.), radar sensors (LiDAR, millimeter-wave radar, ultrasonic sensors, etc.), microphone arrays, and IMU inertial measurement units (accelerometers, gyroscopes, magnetometers, etc.).
The MIPI interface is the primary data channel between camera sensors and the main control chip. Electrical characteristic testing uses high-performance oscilloscopes with automation software to measure key transmitter output parameters such as rise time, fall time, eye opening, and common-mode voltage. C-PHY testing includes parameters such as static point common-mode voltage VCPTX, common-mode voltage mismatch ΔVCMTPX, and intra-pair skew.
Timing characteristic testing verifies switching timing between high-speed (HS) mode and low-power (LP) mode, ensuring that data transmission timing margin meets specification requirements. Eye diagram analysis evaluates long-term signal quality stability by analyzing jitter components.
GMSL and FPD-LINK are used for long-distance video signal transmission. Signal quality testing uses high-speed oscilloscopes to measure parameters such as signal amplitude, frequency response, and impedance matching. Link integrity testing checks bit error rate (BER), link training time, and adaptive equalization performance. Video data integrity testing verifies complete frame transmission and detects frame loss and data errors.
Recommended test equipment includes high-performance oscilloscopes (bandwidth ≥ 3.5GHz), MIPI D-PHY/C-PHY automation test software, low-loading high-impedance probes, and arbitrary waveform generators.
The power module centers on battery packs and cell assemblies. Test objects include individual cells, battery modules, and battery pack systems (including BMS, thermal management devices, and structural parts).
High-precision digital multimeters are used to measure cell terminal voltage in a resting state, with measurement accuracy required to reach the microvolt level. Test items include initial OCV, post-charge/discharge OCV, and long-term OCV monitoring. Acceptance criteria require OCV differences within the same batch of cells to be ≤ 10mV to ensure consistency.
Cell internal resistance testing includes both AC internal resistance and DC internal resistance. AC internal resistance testing applies a small AC signal, typically at 1kHz. DC internal resistance is calculated through pulse charge-discharge testing.
A small sinusoidal excitation signal is applied over a wide frequency range (0.1Hz-1MHz) to measure the frequency response of cell impedance. Test content includes ohmic impedance, charge transfer impedance, and diffusion impedance. Application value includes rapid cell quality screening, consistency evaluation, and aging state monitoring.
Continuous charge-discharge cycle testing is performed on cells to record capacity fade and internal resistance growth curves. Test conditions include standard cycling, accelerated aging, and working-condition simulation. Test indicators include cycle life, capacity retention, and internal resistance growth rate.
Test content includes capacity consistency (deviation ≤ 3%), internal resistance consistency (deviation ≤ 5%), OCV consistency (difference ≤ 10mV), and self-discharge consistency (difference ≤ 10%). Statistical process control methods are used to identify abnormal cells.
Core components of the decision and control module include intelligent chips (CPU, GPU, NPU, and other computing cores), controllers, and ECU electronic control units.
Transmitter testing uses automated test solutions to evaluate transmitter signal quality. For PCIe Gen5 and above, PAM4 signal parameters such as signal-to-noise-and-distortion ratio and uncorrelated jitter need to be tested. Receiver testing performs receiver tolerance tests to verify BER performance under stressed signals. Link testing checks parameters such as transmission loss, impedance continuity, and crosstalk. Protocol testing uses protocol analyzers to verify data transmission integrity.
Physical-layer testing uses oscilloscopes to measure Ethernet electrical characteristics, supporting testing from 10/100/1000BASE-T to 10G/100G Ethernet. Link-layer testing verifies link establishment, maintenance, and disconnection. Network-layer testing checks network topology, routing protocols, and traffic control. QoS testing measures latency, jitter, and packet loss.
End-to-end latency testing measures the total delay from sensor data acquisition to actuator command delivery, typically under 10ms. Data integrity testing uses CRC, parity checking, and other methods to verify transmission correctness. Bandwidth testing checks actual bandwidth utilization of each communication link.
The joint module includes linear actuators (composed of frameless torque motors, ball screws, bearings, etc.) and rotary actuators (composed of frameless torque motors, harmonic reducers, torque sensors, bearings, etc.).
Mixed-signal oscilloscopes together with motor drive analysis software are used to simultaneously capture motor phase voltage, phase current, rotor position, and other signals.
Steady-state performance testing includes torque-speed characteristics, efficiency maps, and power factor. Dynamic performance testing includes starting characteristics, acceleration/deceleration characteristics, and sudden load response. Control strategy verification includes PWM modulation strategy, current-loop control, and speed-loop control.
FOC control verification tests the correctness of field-oriented control algorithms. PID parameter optimization uses step response and frequency response tests to optimize control parameters. Sensorless control testing evaluates startup performance, low-speed performance, and dynamic response of sensorless control algorithms.
Position accuracy testing uses high-precision encoders to measure actual actuator position, including positioning accuracy, repeatability, and backlash. Speed accuracy testing measures speed fluctuation under different speeds. Torque accuracy testing verifies torque control precision and response speed.
Static calibration applies known torque to establish torque-output curves. Dynamic calibration tests sensor response characteristics. Temperature compensation testing evaluates sensor characteristics at different temperatures.
The dexterous hand module includes a drive system (coreless motors, stepper motors, planetary reducers, ball screws, etc.) and a sensing system (tactile sensors, torque sensors, etc.).
Wide-bandgap power devices such as GaN and SiC are widely used in motor drives and require dynamic characteristic testing. Double-pulse testing (DPT) evaluates switching performance. Test content includes switching time, switching loss, reverse recovery, and dynamic on-resistance Rds(on).
Coreless motor characteristic testing includes torque-speed curves, efficiency characteristics, and response time. Stepper motor characteristic testing includes pull-in torque, pull-out torque, and step angle accuracy.
Tactile sensor testing includes spatial resolution, force sensitivity, response time, and linearity. Torque sensor testing includes measurement range, accuracy, repeatability, and hysteresis.
Structural strength testing includes static strength, fatigue strength, and impact strength. Material property testing includes density, elastic modulus, and thermal expansion coefficient.
Wireless communication testing includes Wi-Fi, Bluetooth, and 5G performance. Wired communication testing includes CAN bus testing and RS485 communication testing.
Thermal performance testing includes thermal resistance, heat dissipation capability, and temperature distribution. Fan performance testing includes airflow, static pressure, and noise.
Robot harmonic emission limits (reference standards: GB/T 37669, IEC/TS 61000-3-4): for high-power robotic equipment with per-phase input current greater than 16A, harmonic emission limits have specific requirements. Limits are determined based on short-circuit ratio (SCR).
Robot immunity requirements (reference standards: GB/T 37668, ISO/IEC 23841): as complex systems, robots need EMC performance that also considers functional safety requirements. Immunity levels are determined according to application scenarios, with industrial robots typically requiring higher levels.
Robot-specific test configuration: during testing, the robot should be configured in a typical operating condition, including installation posture, working mode, and load state. Key functions such as motion control, sensor detection, and communication connection should be monitored throughout the test process.
| 11.1 Summary of Required Test Equipment by Module | Test Equipment |
|---|---|
| Intelligent Perception Module | High-performance oscilloscopes (bandwidth ≥ 3.5GHz), MIPI D-PHY/C-PHY test software, low-loading high-impedance probes, arbitrary waveform generators. |
| Power Module | High-precision digital multimeters, electrochemical impedance spectroscopy systems, battery test systems. |
| Decision and Control Module | High-bandwidth oscilloscopes, PCIe test software, BER testers, Ethernet test modules. |
| Joint Module and Dexterous Hand Module | Mixed-signal oscilloscopes, motor drive analysis software, WBG device double-pulse test systems, high-voltage differential probes, current probes. |
| Reliability Test Equipment | High-low temperature chambers, temperature and humidity chambers, salt spray chambers, vibration tables, shock tables, drop testers, power cycling test systems. |
| EMC Test Equipment | EMI receivers, LISN, semi-anechoic/full anechoic chambers, ESD generators, RF signal generators, power amplifiers, EFT/burst generators, surge generators, coupling/decoupling networks (CDN). |
As the ideal carrier of embodied intelligence, AI humanoid robots are entering a critical period of technological breakthroughs and commercialization. Establishing a complete testing system covering the full chain from intelligent perception, power endurance, and decision control to joint actuation and dexterous hand operation, together with reliability and EMC testing, is an important guarantee for product quality and reliability.
Through professional test equipment and analytical methods, R&D teams can quickly identify design issues, optimize product performance, and shorten development cycles. In particular, comprehensive test capability needs to be established in fields such as signal integrity testing, high-speed interface testing, motor drive analysis, dynamic testing of wide-bandgap devices, environmental reliability testing, and EMC testing.
Looking ahead, as humanoid robots move toward higher intelligence and higher reliability, testing requirements will become more diverse and more refined. R&D teams are advised to continuously follow the development of testing technology, establish robust test systems, and strictly comply with national and international standards to lay a solid foundation for the industrial application of humanoid robots.